Prompt Engineering for Large Language Models
Structured prompting, few-shot examples, tool use, and output control.
Structured prompting, few-shot examples, tool use, and output control.
Prompt engineering helps guide large language models to produce accurate, reliable, and consistent outputs using structured instructions and examples.
Define clear roles, goals, constraints, and formats to reduce ambiguity.
Demonstrate desired outputs to help the model replicate patterns.
Guide the model to call APIs, run functions, or use external tools.
Ensure consistency with formats, schemas, and explicit constraints.
Identify the task and success criteria.
Specify rules, tone, and structure.
Demonstrate ideal outputs.
Define tool calls or functions if needed.
Test and adjust prompts for accuracy.
Use structured prompts to pull consistent fields from text.
Use tool-assisted prompting to execute tasks via functions.
Ensure tone, style, and format using output‑controlled prompts.
Quick but inconsistent results.
Improved clarity, reliability, and output control.
Highest accuracy; ideal for complex tasks and workflows.
They reduce ambiguity and guide the model to consistent outputs.
Few‑shot examples help the model learn desired patterns.
They extend the model with actions, calculations, and external data retrieval.
Explicit schemas, formats, or step-by-step instructions.
Master structured prompting, examples, tools, and output control for advanced LLM applications.
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